import http.server
import socketserver
import json
import time
import threading
from urllib.parse import parse_qs, urlparse
import logging
from urllib.parse import parse_qs, urlparse

# Mock responses based on the provided example outputs
MOCK_RESPONSES = [
    # First response (Analyze and Code for file examination)
    {
        "role": "assistant",
        "content": """<Analyze>
To begin this comprehensive data science report, I will first need to understand the structure and content of each provided file. The files appear to relate to payment processing data, including merchant information, transaction fees, and payment records. Here's my initial analysis plan:

1. File Examination:
   - First examine the smallest files (manual.md, payments-readme.md) to understand the data dictionary and business rules
   - Then analyze the core data files (merchant_data.json, payments.csv) which likely contain the main transaction records
   - Finally examine the supporting files (fees.json, acquirer_countries.csv) which likely contain business rules

2. Data Understanding Approach:
   - Start with basic file inspection (size, structure, sample records)
   - Then perform statistical summaries for numerical data
   - Examine categorical value distributions
   - Check for data quality issues

3. Initial Code Plan:
   - First create code to read and summarize each file type (CSV, JSON, MD)
   - Generate descriptive statistics for numerical columns
   - Examine value distributions for categorical columns
   - Check for missing values and data quality issues

I'll begin by examining the manual.md and payments-readme.md files first to understand the data dictionary before diving into the actual data files.

Here's the initial code to examine the manual.md file:
</Analyze>
<Code>
```python
import pandas as pd
import json

# Function to read and display markdown files
def read_md_file(filename):
    with open(filename, 'r') as f:
        content = f.read()
    print(f"Contents of {filename}:")
    print(content[:1000])  # Print first 1000 chars to avoid excessive output

# Read and display manual.md
read_md_file('manual.md')

# Read and display payments-readme.md
read_md_file('payments-readme.md')
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Understand>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Understand>
<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Understand>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Understand>
<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Understand>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Understand>
<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Second response (Analyze and Code for enhanced analysis)
    {
        "role": "assistant",
        "content": """<Understand>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Understand>
<Analyze>
To properly fix and enhance the analysis:

1. Required Fixes:
   - Add missing sklearn imports
   - Ensure all model dependencies are properly imported
   - Verify all model functionality

2. Enhanced Analysis:
   - Add model interpretation visuals
   - Include business impact metrics
   - Add cost-benefit analysis

3. Implementation Plan:
   - Fix imports
   - Add model interpretation visuals
   - Include cost-benefit analysis
   - Generate executive summary visuals

Here's the corrected and enhanced code:
</Analyze>
<Code>
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import precision_recall_curve
from imblearn.over_sampling import SMOTE

# Load data
df = pd.read_csv('payments.csv', nrows=10000)

# Prepare features
features = ['eur_amount','hour_of_day','ip_country','card_scheme','is_credit']
X = pd.get_dummies(df[features])
y = df['has_fraudulent_dispute']

# Handle class imbalance
smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

# Optimized model
param_grid = {
    'n_estimators': [100, 200],
    'max_depth': [None, 10],
    'class_weight': ['balanced', 'balanced_subsample']
}

model = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    scoring='roc_auc',
    cv=3
)
model.fit(X_res, y_res)

# Threshold optimization
y_proba = model.predict_proba(X_res)[:,1]
precision, recall, thresholds = precision_recall_curve(y_res, y_proba)

# Business impact
fraud_costs = df.groupby('card_scheme').agg(
    fraud_rate=('has_fraudulent_dispute','mean'),
    avg_amount=('eur_amount','mean'),
    transaction_count=('psp_reference','count')
)
fraud_costs['potential_savings'] = fraud_costs['avg_amount'] * fraud_costs['fraud_rate'] * fraud_costs['transaction_count'] * 0.7

# Visualization
plt.figure(figsize=(10,6))
plt.plot(thresholds, precision[:-1], label='Precision')
plt.plot(thresholds, recall[:-1], label='Recall')
plt.xlabel('Threshold')
plt.ylabel('Score')
plt.title('Precision-Recall Tradeoff')
plt.legend()
plt.savefig('precision_recall.png')
plt.close()

# Feature importance
importance = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)
importance.sort_values().plot(kind='barh')
plt.title('Feature Importance')
plt.tight_layout()
plt.savefig('feature_importance.png')
plt.close()

print("Optimized model and business analysis complete")
print("Visualizations saved:")
print("- precision_recall.png")
print("- feature_importance.png")
```
"""
    },
    # Third response (Answer with comprehensive analysis)
    {
        "role": "assistant",
        "content": """<Answer>
# Comprehensive Analysis of Payment Processing Fraud Patterns and Predictive Modeling

## Executive Summary

This comprehensive study examines payment processing fraud patterns through advanced machine learning techniques and business analytics. The analysis leverages a dataset of 138,236 transactions across multiple European card schemes, incorporating temporal, geographic, and transactional features. Our methodology combines statistical analysis, machine learning modeling, and business impact assessment to provide actionable insights for fraud prevention strategies. The research demonstrates that while current fraud detection systems achieve high precision (93%) in identifying legitimate transactions, they face significant challenges in fraud detection (recall of 5%). The analysis reveals substantial variation in fraud patterns across card schemes, with GlobalCard showing the highest potential savings of €16,853 when implementing optimized fraud prevention measures. The study contributes both methodological innovations in imbalanced classification and practical business insights for payment processors.

## Data Characteristics and Methodology

The dataset comprises synthetic transaction records spanning a full calendar year, with transactions recorded at minute-level granularity. The dataset includes 15 key features across temporal, geographic, and transactional dimensions. Temporal features include hour_of_day (range: 0-23), minute_of_hour (0-59), and day_of_year (1-365). Geographic features capture both shopper location (ip_country) and card issuer location (issuing_country). Transactional features include monetary amount (eur_amount ranging from €1.12 to €4,920), payment method (is_credit), and card scheme (4 categories). The dataset contains 8 unique merchant categories and 5 account types, with fraud indicators (has_fraudulent_dispute) showing a prevalence of 7.25-8.27% across card schemes.

Our analytical approach employed a three-phase methodology: exploratory data analysis, predictive modeling, and business impact assessment. The exploratory phase revealed significant temporal patterns, with transaction volumes peaking at 13:00 (2,843 transactions) and troughing at 03:00 (1,012 transactions). Geographic analysis showed IP countries SE (23.4%), NL (21.1%), and FR (19.8%) as the most frequent. Transaction amounts followed a right-skewed distribution (mean: €91.92, SD: €113.72), with credit card transactions averaging €94.31 versus debit card's €89.47 (t=0.86, p=0.39).

## Fraud Pattern Analysis and Predictive Modeling

The fraud detection modeling employed a Random Forest classifier with SMOTE oversampling to address class imbalance (fraud rate: 7.3%). The optimized model achieved ROC AUC of 0.6253, with precision of 0.93 for legitimate transactions and 0.05 for fraudulent cases. Feature importance analysis revealed hour_of_day (0.32), eur_amount (0.28), and ip_country (0.19) as most predictive. The precision-recall tradeoff analysis showed optimal operating thresholds at 0.35, balancing 0.82 precision with 0.42 recall. The confusion matrix revealed 2,775 true negatives (97% specificity) but only 11 true positives (5% recall).

Card scheme analysis revealed significant variation in fraud characteristics. GlobalCard showed highest fraud rate (7.55%) but lowest average transaction amount (€90.51), while TransactPlus showed lowest fraud rate (7.25%) but highest average amount (€92.48). The cost-benefit analysis projected €16,853 potential savings for GlobalCard, €14,400 for NexPay, €11,718 for TransactPlus, and €4,954 for SwiftCharge under a 30% fraud reduction scenario.

## Business Impact and Implementation Strategy

The analysis suggests three strategic recommendations for fraud prevention: (1) temporal-based fraud controls during high-risk hours (18:00-06:00), (2) geographic-based controls for high-risk countries (FR, NL, DE), and (3) scheme-specific fraud thresholds. The model's feature importance suggests incorporating additional features like device fingerprinting and behavioral analytics could improve recall. The business case shows that implementing the optimized model could reduce fraud costs by €47,925 annually, with implementation costs of €85,000 yielding 6-month ROI.

## Limitations and Future Research

While this study provides comprehensive fraud pattern analysis, several limitations merit discussion. The synthetic nature of the dataset may limit generalizability, and the 30% fraud reduction assumption requires validation. Future research should incorporate real-world transaction data and longitudinal analysis of fraud pattern evolution. Additional research directions include deep learning approaches for fraud detection and real-time scoring systems. The methodology presented here provides a robust framework for payment processor fraud management that balances security with customer experience.

## Conclusion

This study demonstrates that data-driven fraud prevention requires multidimensional approaches combining temporal, geographic, and behavioral factors. The 0.6253 ROC AUC achieved by our model, while not perfect, represents a significant improvement over rule-based systems (0.55). The €47,925 annual savings potential demonstrates the business value of advanced analytics in payment processing. Future work should focus on real-time implementation architectures and continuous model improvement through concept drift detection. The findings provide both methodological contributions to fraud analytics and practical insights for payment processors seeking to optimize fraud prevention strategies.

# 测试 Markdown 渲染：复杂结构示例

## 引言
这是一个用于测试 Markdown 渲染的复杂示例，包含多种元素，如**粗体**、*斜体*、~~删除线~~ 和 `内联代码`。

### 子标题三级
- 无序列表项 1
  - 嵌套无序列表项
- 无序列表项 2
  - 另一个嵌套项
    - 更深嵌套

1. 有序列表项 1
2. 有序列表项 2
   1. 嵌套有序
   2. 另一个嵌套

#### 子标题四级
任务列表：
- [x] 已完成任务
- [ ] 未完成任务
- [x] 另一个已完成

---

## 表格示例
| 列 1 | 列 2 | 列 3 |
|------|------|------|
| 行 1，单元格 1 | 行 1，单元格 2 | 行 1，单元格 3 |
| 行 2，单元格 1 | **粗体单元格** | [链接](https://example.com) |
| 行 3，单元格 1 | *斜体单元格* | `代码单元格` |

### 代码块示例
```python
def hello_world():
    print("Hello, World!")
    if True:
        for i in range(3):
            print(f"循环 {i}")
```

#### 嵌套代码块
在段落中内嵌代码：`print("内嵌")`。

## 引用和分隔线
> 这是一个块引用。
> 它可以多行。
> 
> 引用结束。

---

另一个引用：
> > 嵌套引用示例。

## 链接和图片
- [外部链接](https://www.example.com)
- [锚点链接](#引言)（返回顶部）

![测试图片](https://via.placeholder.com/150x100?text=Markdown+Test) <!-- 占位符图片 -->

### 表情和特殊字符
使用表情：😊👍

特殊字符：© ® ™ & < > 

## 脚注示例[^1]
这是一个带有脚注的句子[^2]。

[^1]: 脚注 1：这是第一个脚注。
[^2]: 脚注 2：这是第二个脚注，位于表格下方。

## 数学公式（如果支持）
内联公式：$E = mc^2$。

块公式：
$$
\\int_{-\\infty}^{\\infty} e^{-x^2} dx = \\sqrt{\\pi}
$$

### 结束语
这个结构测试了 Markdown 的各种渲染功能。如果一切正常，你应该看到格式化的输出！

</Answer>"""
    }
]


class MockOpenAIHandler(http.server.BaseHTTPRequestHandler):
    def do_POST(self):
        try:
            # Set socket timeout to prevent hanging
            self.wfile._sock.settimeout(5.0)
            content_length = int(self.headers.get('Content-Length', 0))
            post_data = self.rfile.read(content_length).decode('utf-8')
            logging.debug(f"Received request: {post_data[:1000]}...")
            request_data = json.loads(post_data)

            if self.path == "/v1/chat/completions":
                messages = request_data.get('messages', [])
                stream = request_data.get('stream', False)
                assistant_messages = [msg for msg in messages if msg['role'] == 'assistant']
                
                response_index = min(len(assistant_messages), len(MOCK_RESPONSES) - 1)
                print(response_index)
                response_content = MOCK_RESPONSES[response_index]['content']
                logging.debug(f"Selected response index: {response_index}, stream: {stream}")

                if stream:
                    # Streaming response
                    self.send_response(200)
                    self.send_header('Content-Type', 'text/event-stream')
                    self.send_header('Cache-Control', 'no-cache')
                    # Omit 'Connection: keep-alive' to allow connection closure
                    self.end_headers()

                    # Send chunks
                    chunks = [response_content[i:i+10] for i in range(0, len(response_content), 10)]
                    for i, chunk in enumerate(chunks):
                        chunk_data = {
                            "id": f"chatcmpl-{i}",
                            "object": "chat.completion.chunk",
                            "created": int(time.time()),
                            "model": request_data.get('model', 'mock-model'),
                            "choices": [
                                {
                                    "index": 0,
                                    "delta": {"content": chunk},
                                    "finish_reason": "stop" if i == len(chunks) - 1 else None
                                }
                            ]
                        }
                        try:
                            self.wfile.write(f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8'))
                            self.wfile.flush()
                            logging.debug(f"Sent chunk {i+1}/{len(chunks)}")
                            time.sleep(0.1)
                        except BrokenPipeError:
                            logging.error("Client disconnected during streaming")
                            break

                    # No [DONE] message; close connection to signal end
                    logging.debug("Finished streaming, closing connection")
                else:
                    # Non-streaming response
                    self.send_response(200)
                    self.send_header('Content-Type', 'application/json')
                    self.send_header('Connection', 'close')
                    self.end_headers()

                    response_data = {
                        "id": "chatcmpl-123",
                        "object": "chat.completion",
                        "created": int(time.time()),
                        "model": request_data.get('model', 'mock-model'),
                        "choices": [
                            {
                                "index": 0,
                                "message": {
                                    "role": "assistant",
                                    "content": response_content
                                },
                                "finish_reason": "stop"
                            }
                        ]
                    }
                    self.wfile.write(json.dumps(response_data).encode('utf-8'))
                    self.wfile.flush()
                    logging.debug("Sent non-streaming response")
            else:
                self.send_response(404)
                self.send_header('Connection', 'close')
                self.end_headers()
                self.wfile.write(b"Endpoint not found")
                logging.error(f"Invalid endpoint: {self.path}")
        except Exception as e:
            logging.error(f"Error handling request: {str(e)}")
            self.send_response(500)
            self.send_header('Connection', 'close')
            self.end_headers()
            self.wfile.write(f"Error: {str(e)}".encode('utf-8'))
            self.wfile.flush()
        finally:
            try:
                self.wfile._sock.close()
                logging.debug("Connection closed")
            except:
                logging.error("Error closing socket")

def run_server(port=8000):
    server_address = ('', port)
    httpd = socketserver.TCPServer(server_address, MockOpenAIHandler)
    httpd.timeout = 10.0
    logging.info(f"Mock OpenAI API server running on port {port}")
    httpd.serve_forever()

if __name__ == "__main__":
    threading.Thread(target=run_server, daemon=True).start()
    logging.info("Mock API server started")
    try:
        while True:
            time.sleep(1)
    except KeyboardInterrupt:
        logging.info("Shutting down mock API server")